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Seven Pillars of Big Data

The three V words commonly used to describe Big Data – volume, velocity and variety – fail to fully describe the whole concept of Big Data, according to the chief data scientist of oilfield service firm Halliburton.

Oil and gas companies are increasingly turning to Big Data as a means of better using structured and unstructured data generated by operations not only to enhance safety, efficiency and productivity, but to predict events before they happen.

Instead of the three Vs, Big Data is more accurately described by the seven Vs, or the Seven Pillars of Big Data, said Dr. Satyam Priyadarshy, chief data scientist for Halliburton’s Landmark division, in an interview with Rigzone. Besides volume, variety and velocity, the other Vs, or pillars of Big Data, include: veracity, virtual, variability and value.

The three Vs can actually be confusing terms, not actually describing the whole concept of Big Data. Volume and velocity can both be high or low, and variety now equals to all data, not just unstructured and structured data, said Priyadarshy.

The fourth V, veracity – or correctness or accuracy of data or context of the data – is critical.

“If someone changes data to conform to their ‘verifiable’ standards then that data is no longer raw,” Priyadarshy explained.

Virtual – the fifth V – addresses the challenges of moving data. In the past, data was moved from place to place for analysis. But the high cost of transporting that ever-increasing data makes this model unsustainable in oil and gas.

“The goal is to create new patterns from connecting various attributes from different data sets and, as a result, one does not need to bring and copy the data from one storage to another just for analytics. We need to leverage the compute power of the technology and reduce the cost of moving data.”

The sixth V – variability – can occur in any of the Vs, depending on the stage of the exploration and production lifecycle. For example, seismic data is generated in high volume, low velocity and the associated value of seismic data interpretation is high. In other situations, the velocity and variety of drilling data may be low, but the value may be significant.

The value of data – the seventh V – is also critical. For example, a mobile phone can generate lots of data, but if a company is not using the data, it has no value for them. Data needs to have value for a business, said Priyadarshy.